Status of Regulation on the Per‐ and Polyfluoroalkyl Substances Across the Globe
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) have been considered global environmental contaminants due to their persistence, bioaccumulation, and potential adverse health effects. This chapter provides an extensive overview of the regulatory status of PFAS worldwide. It explores country-specific regulations, including the United States, the European Union (EU), the United Kingdom, Canada, Australia, India, Japan, and other developing nations. The chapter also examines the roles of global organizations such as the Stockholm Convention, the United Nations Environment Programme UNEP), the Organisation for Economic Cooperation and Development (OECD), and the International Pollutants Elimination Network (IPEN)in shaping international policies. The discrepancies in regulatory standards and guidelines and the scientific, economic, and political factors influencing these disparities are also analyzed. Further, the key challenges in PFAS regulation, encompassing technological constraints, data deficiencies, and industrial resistance, are also discussed. Finally, the chapter delineates future perspectives on harmonizing global standards, advancing safer alternatives, and strengthening capacity in developing regions. This chapter, therefore, underscores the pressing need for coordinated global efforts to manage PFAS risks effectively and sustainably.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".